Background:Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection.Patients and methods:Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles.Results:GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%).Conclusions:Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis.
背景：肺癌是全球癌症相关死亡的主要原因。目前缺乏能够改善疾病预后的有效早期诊断筛查策略。挥发性有机化合物的无创呼气分析是早期检测肺癌的潜在方法。本研究探索将挥发性有机化合物谱与人工智能相结合，以实现一种灵敏、特异且快速的肺癌检测方法。 患者与方法：在两个临床中心收集了123名健康个体和73名肺癌患者的呼出气体样本。入组患者均为经确诊的不同分期肺癌病例。所有样本均在患者接受任何治疗（包括手术）前采集，并采用气相色谱-离子迁移谱联用技术进行分析。通过人工智能方法对整体色谱谱图进行分类。 结果：气相色谱-离子迁移谱联用技术具有高灵敏度，能生成详细的色谱谱图。人工智能方法通过训练和验证步骤对两组呼出气体谱图进行分级分类，过程中刻意排除且未受定性信息影响。K最近邻算法对两组的分类准确率达90%（灵敏度=87%，特异性=92%）。将肺癌组限定为仅IA早期患者时，准确率仍保持90%（灵敏度=90%，特异性=93%）。 结论：采用人工智能算法评估整体呼出气体谱图能够实现肺癌检测，并证明定性信息可能非必需条件，这缓解了目前许多研究面临的困境。结果表明该方法结合筛查方案可改善肺癌的早期检测，从而改善疾病预后。
肿瘤(癌症)患者之家